Normal brain aging and Alzheimer's disease are associated with lower cerebral pH: an in vivo histidine 1H-MR spectroscopy study

Neurobiology of Aging xxx (2019) 1e10

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Normal brain aging and Alzheimer's disease are associated with lower cerebral pH: an in vivo histidine 1H-MR spectroscopy study Epameinondas Lyros a, *, Andreas Ragoschke-Schumm a, Panagiotis Kostopoulos a, c, Alexandra Sehr a, Martin Backens b, Stefania Kalampokini a, Yann Decker a, Martin Lesmeister a, Yang Liu a, Wolfgang Reith b, Klaus Fassbender a, * a b c

Department of Neurology, Saarland University Clinic, Homburg, Germany Department of Neuroradiology, Saarland University Clinic, Homburg, Germany Medical Park Bad Camberg, Germany

a r t i c l e i n f o

a b s t r a c t

Article history: Received 17 May 2019 Received in revised form 9 November 2019 Accepted 17 November 2019

It is unclear whether alterations in cerebral pH underlie Alzheimer's disease (AD) and other dementias. We performed proton spectroscopy after oral administration of histidine in healthy young and elderly persons and in patients with mild cognitive impairment and dementia (total N ¼ 147). We measured cerebral tissue pH and ratios of common brain metabolites in relation to phosphocreatine and creatine (Cr) in spectra acquired from the hippocampus, the white matter (WM) of the centrum semiovale, and the cerebellum. Hippocampal pH was inversely associated with age in healthy participants but did not differ between patients and controls. WM pH was low in AD and, to a lesser extent, mild cognitive impairment but not in frontotemporal dementia spectrum disorders and pure vascular dementia. Furthermore, WM pH provided incremental diagnostic value in addition to N-acetylaspartate to Cr ratio. Our study suggests that in vivo assessment of pH may be a useful marker for the differentiation between AD and other types of dementia. Ó 2019 Elsevier Inc. All rights reserved.

Keywords: Brain aging Alzheimer's disease pH White matter MR spectroscopy

1. Introduction Many factors could influence the onset and the time course of pathophysiological changes occurring in the brain of patients with Alzheimer's disease (AD) and other dementias. Among these factors, cerebral pH may be important because protein folding and enzyme activity are very sensitive to changes in pH, and low pH can enhance the aggregation of proteins, such as amyloid peptide (Atwood et al., 1998; Barrow and Zagorski, 1991; Burdick et al., 1992). Magnetic resonance spectroscopy (MRS) provides a noninvasive method of assessing the pH of cerebral tissue in vivo. Brain pH can be measured with either phosphor (31P) MRS or proton (1H) MRS after oral administration of histidine to increase its concentration in the brain. Proton (1H) MRS takes advantage of the existence of various protonation states of the imidazole ring of histidine; these protonation states produce a chemical shift that is used to calculate pH (Vermathen et al., 2000). * Corresponding author at: Department of Neurology, Saarland University Clinic, Kirrberger Straße Geb. 90, D-66421 Homburg, Germany. Tel.: 00496841/1624103; fax: 00496841/1624137. E-mail addresses: [email protected] (E. Lyros), Klaus.Fassbender@uks. eu (K. Fassbender). 0197-4580/$ e see front matter Ó 2019 Elsevier Inc. All rights reserved. https://doi.org/10.1016/j.neurobiolaging.2019.11.012

Only a few MRS studies of pH changes in dementia have been performed and they have produced conflicting results. Earlier studies have involved only a few cases, had various technical limitations, and were restricted only to the hippocampus (HC). Although one study reported an increase in pH in the left HC of patients with AD (N ¼ 7) (Mecheri et al., 1997), another study found a nonstatistically significant trend toward lower pH among patients with mild cognitive impairment (MCI) but no significant difference in the pH of the HC between control participants and patients with AD (MCI, N ¼ 5; AD, N ¼ 6) (Mandal et al., 2012). More recently, a larger phosphorus spectroscopy study which included pH assessments in the HCs, the retrosplenial cortex, and the anterior cingulate cortex of 31 patients with mild AD reported an on average slightly increased pH in patients compared with controls (Rijpma et al., 2018). In the study reported here, we used 1H-MRS after oral administration of histidine to comparatively evaluate pH levels, in addition to levels of common brain metabolites, in various brain areas of patients with AD or other dementias and of cognitively normal young and elderly subjects. We further investigated into the clinical utility of brain pH in the differential diagnosis of dementias.

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information documents were approved by the Ethics Committee of the Medical Association of the Saarland, Germany (ID number 03/ 07).

2. Methods 2.1. Subjects The study involved 147 subjects. Patients were recruited from the Department of Neurology of the University Hospital of the Saarland. Two healthy control groups were included, one consisting of young subjects aged 20e42 years (n ¼ 17) and the other of older subjects aged 49e82 years (n ¼ 30). Patients were placed into one of 5 groups: (1) patients with AD (n ¼ 27); (2) patients with frontotemporal dementia (FTD) spectrum disorders (n ¼ 15), including 9 patients with behavioral type FTD, 2 with primary progressive aphasia, 2 with corticobasal degeneration, and 2 with progressive supranuclear palsy; (3) patients with pure vascular dementia (VaD) (n ¼ 10); (4) patients with mixed AD and VaD (n ¼ 21); and (5) patients with MCI (n ¼ 27). Patient characteristics are displayed in Table 1. All participants were evaluated by a neurologist experienced in the field of dementias. This neurologist evaluated participants' medical history and the results of physical examination, structural brain MRI, laboratory testing, including in many cases an analysis of cerebrospinal fluid, and neuropsychological assessment through the German version of the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) (Schmid et al., 2014). Clinical data were collected before MRS analysis, thus blinding the neurologist to these results. Diagnoses were made according to established clinical diagnostic criteria of the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV) (American Psychiatric Association, 1994); the AD criteria of the National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer's Disease and Related Disorders Association (McKhann et al., 1984); the consensus criteria for frontotemporal lobar degeneration (FTLD) (Neary et al., 1998); the corticobasal degeneration criteria of Boeve (Boeve et al., 2003); the progressive supranuclear palsy criteria of the National Institute of Neurological Disorders and Stroke (NINDS) Society for Progressive Supranuclear Palsy (Litvan et al., 1996); and the VaD criteria of NINDS and the Association Internationale pour la Recherche et l’Enseignement en Neurosciences (NINDS-AIREN) (Roman et al., 1993). However, we excluded VaD patients with severe territorial infarcts and included in this category only patients with small-vessel disease. The diagnosis of MCI was made according to the criteria of Petersen (Petersen, 2004). Overall cognitive ability was assessed with the Mini-Mental State Examination (MMSE). To control for the possible effects of chronic brain ischemia on pH, the burden of white matter (WM) lesions was assessed with the Fazekas Scale (Fazekas et al., 1993) on an axial fluid-attenuated inversion recovery sequence of an MRI scan obtained at 1.5 or 3 Tesla (T). All participants provided written informed consent. The study protocol, the informed consent document, and the subject

2.2. Magnetic resonance spectroscopy Histidine-based 1H-MRS was performed according to the protocol of Vermathen et al., 2000 and was used to assess pH in 3 distinct cerebral regions: WM in the centrum semiovale, the HC region, and the cerebellum (CB). Participants were pretreated with an oral histidine solution (400 mg/kg in 1 L of tea) 4e7 hours before examination (L-histidine; C6H9N3O2, Fagron BV, Rotterdam, the Netherlands). To prevent zinc deficiency resulting from the formation of chelate complexes, we administered an additional 2 mg of zinc orally for each 100 mg of histidine (zinc orotate-dihydrate DAC; Fagron). Scans were performed with a 1.5 T MR scanner (Magnetom Sonata, Software Version VA21A; Siemens, Germany) and a 3T MR scanner (Magnetom Skyra, Software Version D11; Siemens). A circular polarized head array coil was used with the Sonata scanner, and a 20-channel head/neck 20 coil was used with the Skyra scanner. 2.3. Scanning protocol and spectral data processing We used a T2 true fast imaging localizer to obtain images of 20e30 slices (each 5 mm thick) in the axial, coronal, and sagittal planes. Single-voxel spectroscopy was performed with a 20 mm  25 mm  25 mm voxel in the WM of the left hemisphere next to the lateral ventricle; a 23 mm  25 mm  22 mm voxel in the left hemisphere of the CB; and an 18 mm  30 mm  20 mm in the right HC (Fig. 1). The WM voxel was placed in close proximity laterally to the left lateral ventricle, orientated in the anterior-posterior direction. The rectangular-shaped CB voxel was placed in the left cerebellar hemisphere and rotated so as not to exceed the borders of the CB. The elongated voxel of the HC was orientated along the inferior horn of the right lateral ventricle. In cases of severe atrophy, the voxel size was reduced appropriately. A point-resolved spectroscopy sequence was used with an echo time of 30 ms and a repetition time (TR) of 1500 ms. The total measurement time ranged from 55 to 65 minutes. Shims were placed manually so that the highest possible field homogeneity could be obtained. In each localization, measurements with 250 averages were performed. We examined patients before and after histidine intake to allow for clearer identification of the histidine peaks by subtraction of the respective spectra. As in the study of Vermathen et al., the 2 peaks at 7.8 and 7.1 ppm were clearly increased in the spectrum after histidine uptake (Fig. 1B III). Intracellular pH was calculated from the chemical shift of the 2 histidine imidazole resonances and from their difference. We applied the HendersonHasselbalch equation, as described by Vermathen et al., 2000.

Table 1 Demographic characteristics and pH data of the participant groups Groups

N total

Age

Young adults Old adults AD VaD Mixed type FTLD MCI

17 30 27 10 21 15 27

25.3 65.2 68.6 75.8 77.8 61.9 65.7

      

5.6 8.3 9.9 8.2 4.7 8.1 8.2

Sex (M/F)

Device (1.5 T/3 T)

MMSE

5/12 9/21 13/14 7/3 10/11 12/3 17/10

9/8 6/24 17/10 2/8 12/9 3/12 14/13

30.00 29.07 21.69 23.1 21.89 24.27 27.5

      

pH_WM 0.00 1.17 6.14 4.77 5.27 5.65 1.92

n n n n n n n

¼ ¼ ¼ ¼ ¼ ¼ ¼

17 30 26 10 19 15 26

6.91 6.91 6.84 6.91 6.87 6.91 6.86

      

0.04 0.06 0.05 0.06 0.05 0.04 0.05

pH_HC n n n n n n n

¼ ¼ ¼ ¼ ¼ ¼ ¼

17 30 26 10 21 15 27

6.91 6.87 6.88 6.92 6.86 6.88 6.88

      

pH_CB 0.05 0.05 0.09 0.06 0.09 0.07 0.05

n n n n n n n

¼ ¼ ¼ ¼ ¼ ¼ ¼

14 24 17 4 15 12 23

6.87 6.87 6.88 6.85 6.87 6.85 6.87

      

0.04 0.05 0.07 0.08 0.04 0.06 0.07

n n n n n n n

¼ ¼ ¼ ¼ ¼ ¼ ¼

12 24 18 6 17 12 18

The values are described as mean  SD. The number of cases on which each measurement was based is indicated by n next to the value. Key: M/F, male/female ratio, AD, Alzheimer’s disease; VaD, vascular dementia, FTLD, frontotemporal lobar degeneration; MCI, mild cognitive impairment; mixed type dementia is mixed AD and VaD; pH_WM, pH in white matter; pH_HC, pH in the hippocampus; pH_CB, pH in the cerebellum.

The values are T1- and T2-corrected and refer to measurements as performed after histidine administration. The number of cases on which calculations for each region were based is indicated by n on the left of the columns containing the values. Key: NAA, N-acetylaspartate; Cr, creatine, Cho, choline; mI, myo-inositol; AD, Alzheimer’s disease; VaD, vascular dementia; FTLD, frontotemporal lobar degeneration; MCI, mild cognitive impairment.

       0.68 0.59 0.64 0.61 0.58 0.60 0.62        0.23 0.23 0.24 0.22 0.23 0.22 0.23 0.11 0.12 0.17 0.09 0.10 0.10 0.11        0.77 0.75 0.89 0.89 0.86 0.79 0.85 0.03 0.03 0.02 0.04 0.03 0.04 0.03        0.26 0.28 0.29 0.27 0.29 0.29 0.29 0.20 0.21 0.16 0.17 0.20 0.23 0.19               0.97 0.95 1.11 1.02 1.05 1.06 1.04 0.04 0.03 0.03 0.05 0.04 0.02 0.05        0.28 0.31 0.31 0.30 0.31 0.34 0.33 0.31 0.18 0.17 0.12 0.17 0.19 0.31        Young adults Old adults MCI AD Mixed type VaD FTLD

n n n n n n n

¼ ¼ ¼ ¼ ¼ ¼ ¼

14 27 25 20 15 7 10

1.50 1.16 1.23 1.23 1.27 1.10 1.18

Cho/Cr NAA/Cr

Hippocampus Participant group

Table 2 Metabolite ratios across participants’ groups per brain region (mean  SD)

mI/Cr

0.15 0.15 0.18 0.30 0.24 0.14 0.20

n n n n n n n

¼ ¼ ¼ ¼ ¼ ¼ ¼

17 29 27 26 21 10 14

1.64 1.61 1.52 1.37 1.49 1.51 1.42

Cho/Cr NAA/Cr

White matter of the centrum semiovale

0.09 0.11 0.18 0.20 0.18 0.15 0.13

n n n n n n n

¼ ¼ ¼ ¼ ¼ ¼ ¼

11 26 19 17 17 7 13

1.05 0.99 0.99 0.93 0.88 0.90 0.86

NAA/Cr

      

Cerebellum

mI/Cr

Cho/Cr

0.03 0.02 0.03 0.04 0.03 0.03 0.02

mI/Cr

0.11 0.09 0.11 0.15 0.10 0.14 0.12

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We measured brain metabolite signals detected upfield to water, namely N-acetylaspartate (NAA), choline (Cho)-containing compounds, phosphocreatine and creatine (Cr), and myo-inositol (mI). Quantitative metabolite data were estimated on the basis of fitted peak areas. The peak areas were calculated by automatic curve fitting with the LCModel software (Stephen Provencher Inc., Ontario, Canada) (Provencher, 1993). Peak area ratios, by which the signal intensity of one metabolite is expressed as a fraction of another, are reported relative to the signal of Cr, which was used as an internal reference. To assess possible changes in the concentration of standard metabolites after histidine administration, we compared metabolite signals in spectra obtained before and after histidine loading in a subset of 89 subjects and found no significant differences between measurements made before and after histidine uptake (data not shown). Metabolite concentrations were therefore assumed to be unchanged by histidine consumption. Because T1 and T2 relaxation times of metabolites differ significantly between 1.5 T and 3T, we applied T1 and T2 corrections to the signal intensities of peaks (Mills et al., 1987) and calculated metabolite ratios based on these corrected values to enable a uniform analysis of metabolite signals irrespective of field strength. T1 and T2 correction was carried out for each metabolite separately by calculating M0 according to the following equation for double-echo sequences:

M ¼ M0 $ R with R ¼ expðTE = T2Þ ½1  expðTR = T1Þ þ 2 exp fðTE = 2  TRÞ = T1g  2 expfð3TE = 2  TRÞ = T1g where M represents the measured peak intensity and M0 the peak intensity corrected for relaxation times. Values for T1 and T2 were chosen as mean values from the literature (Ethofer et al., 2003; Ganji et al., 2012; Li et al., 2008; Mlynarik et al., 2001; Steinke et al., 2017; Traber et al., 2004; Tsai et al., 2007).

2.4. Statistical analysis All statistical analyses were performed with IBM SPSS Statistics for Windows, version 20 (IBM Corp., Armonk, NY, USA) with significance set at p < 0.05. We tested for associations between normally distributed variables using Pearson Product-Moment Correlation. Repeated measures ANOVAs were used to test for variations in pH across examined regions of interest (ROIs). To compare pH levels and metabolite ratios between groups, we performed a multivariate analysis of covariance using as covariates the participants' age and sex, as well as the device used (1.5 T or 3T) followed by separate univariate analysis of covariances, which also controlled for the effects of the aforementioned factors. In the case of statistically significant results, we performed post hoc tests corrected for multiple comparisons to determine which pairs of groups were significantly different. To account for multiple testing in the case of exploratory correlation analyses, p-values were false discovery ratedcorrected at the level of 5%. We performed receiver operating characteristics (ROC) analyses to determine whether alterations in pH or metabolite ratios can distinguish patients from control participants or patients with one type of dementia type from those with another. To evaluate whether the combination of pH and one metabolite ratio can serve as a marker for detecting disease, we performed logistic regression analyses to test the predicted probabilities, using the 2 candidate markers as explanatory variables.

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Fig. 1. MR Spectroscopy. (A) Positioning of the 1H MRS voxel on T2-weighted images in all 3 planes (from left to right): axial, coronal, and sagittal in each of the 3 brain regions (from above to below): (I) in the white matter of centrum semiovale, (II) in the hippocampus, and (III) in the cerebellum. (B) (I) Example of 1H MRS spectrum showing histidine resonances downfield from water, as well as resonances of brain metabolites N-acetylaspartate (NAA), creatine, choline, and myo-inositol (mI) in the upfield spectral region. (II) Representative spectra from each brain region at each field strength with the quality of fits shown for other metabolites than histidine (upper panel from left to right WM 1.5 T, HC 1.5 T, CB 1.5 T, and lower panel from left to right WM 3.0 T, HC 3.0 T, CB 3.0 T). (III) Representative histidine difference spectra between before and after injection of histidine solution from which pH values were obtained for each brain region at each field strength (upper panel from left to right WM 1.5 T, HC 1.5 T, CB 1.5 T and lower panel from left to right WM 3.0 T, HC 3.0 T, CB 3.0 T).Abbreviations: MRS, magnetic resonance spectroscopy; WM, white matter; HC, hippocampus; CB, cerebellum.

3. Results We successfully measured the 1H-MRS data in the WM of 146 subjects, in the CB of 107 subjects, and in the HC of 109 subjects. The reasons for this imbalance in participants were (1) our strict criteria for evaluation of signal quality (spectra with a signal-to-noise ratio of less than 10 or a spectral variance of more than 10% for NAA, Cr, or

Cho were rejected); (2) the use of the MRI devices in the everyday clinical setting imposed some time constraints; and (3) in some cases, the participants' compliance with the long acquisition protocol for the collection of data from all 3 locations of the brain was reduced. Little's Missing Completely at Random Test for all pH and metabolite ratio values was not statistically significant (p ¼ 0.372) indicating that any missing data were missing in a random way and

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Fig. 1. (continued).

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not systematically. We measured 150 spectroscopy volumes with the 1.5 T scanner and 212 with the 3T scanner. The chemical shift displacement for the histidine resonance relative to NAA is 25% of voxel size in 1.5 T and 50% of voxel size in 3 T. 3.1. pH in association with regions of interest and healthy brain aging ANOVA with repeated measures was used to analyze data from normal participants and showed that ROI exerted a statistically significant overall effect on pH levels (F ¼ 6.30; p ¼ 0.006). Post hoc paired-sample t-tests showed that pH was significantly higher in WM than in CB (6.91  0.04 vs 6.87  0.05; t ¼ 4.53; p < 0.001), whereas there were no other statistically significant differences. An intercorrelation analysis within normal participants showed a moderate positive correlation between the pH levels in WM and CB (Pearson r ¼ 0.457; p ¼ 0.005) and a tendency toward a correlation between pH levels in WM and HC (Pearson r ¼ 0.280; p ¼ 0.089). Our examination of the effect of age on brain pH showed a statistically significant inverse correlation between age and pH of the HC (Pearson r ¼ 0.372; p ¼ 0.021). The correlational equation suggested an average decrease in pH of the HC of 0.01 per decade. There was no correlation between age and pH levels in other ROIs. 3.2. pH and metabolite ratio differences between diagnostic groups Multivariate analysis of covariance adjusted for age, sex, and the scanner used was conducted to test for differences between groups of different dementia types, MCI, and normal old adults on MRS measures and revealed that group membership had a significant effect on the set of the 12 variables tested (pH, NAA/Cr, Cho/Cr, and mI/Cr in the 3 brain regions) with a Pillai's trace value of 0.74 (p ¼ 0.002). Subsequent ANOVAs showed a statistically significant overall effect of group classification on the WM pH level (F ¼ 6.24; p < 0.001) but not on the pH level in other brain regions. Post hoc multiple comparisons with Tukey honestly significant difference tests found the following between-group differences (see also Table 1 and Fig. 2): the WM pH level was significantly lower in patients with AD than in normal adults (p < 0.001), in patients with VaD (p ¼ 0.008), and in patients with FTLD (p ¼ 0.003). Furthermore, the WM pH level was also lower in patients with MCI than in normal adult participants (p ¼ 0.015). In contrast, the pH levels in the brain regions of patients with FTLD or patients with pure VaD did not differ significantly from the pH levels in any of the examined brain regions in the control group. Regarding the comparison of T1- and T2-corrected metabolite ratios as measured after histidine administration across groups (see Table 2), analysis of covariances showed a statistically significant overall effect for the WM NAA/Cr ratio (F ¼ 4.06; p ¼ 0.002) and for the CB NAA/Cr ratio (F ¼ 3.10; p ¼ 0.011), but no significant effect for the other metabolite ratios. Post hoc intergroup comparisons showed that the WM NAA/ Cr ratio was significantly lower in patients with AD than in normal control participants (p < 0.001) or in patients with MCI (p ¼ 0.047); the WM NAA/Cr ratio was also significantly lower in patients with FTLD than in normal control participants (p ¼ 0.027). The CB NAA/ Cr ratio was lower in patients with FTLD than in normal control participants (p ¼ 0.034) or in patients with MCI (p ¼ 0.036). 3.3. Correlations between pH, metabolite ratios, and clinical data in cognitively impaired subjects Within the combined group of cognitively impaired subjects (all types of dementia and MCI patients), there were no significant correlations between pH values and corresponding metabolite ratios and also no correlation between pH and MMSE scores or scores

on the various CERAD battery subtests even before correction for multiple comparisons. Furthermore, pH did not correlate with burden of WM lesions on MRI as rated visually using the Fazekas score. No differences in pH were detected between groups of patients with AD according to age at onset of disease. A correlation analysis between metabolite measures and neuropsychological test performances within the combined group of cognitively impaired subjects showed that WM NAA/Cr ratio correlated with the degree of cognitive impairment as reflected in the MMSE score (Pearson r ¼ 0.273; unadjusted p ¼ 0.008) and the z-scores of the Trail Making Test part A and phonemic fluency (S-words) subtest of the CERAD battery (Pearson r ¼ 0.322, unadjusted p ¼ 0.006 and r ¼ 0.332, unadjusted p ¼ 0.005, respectively). There was also a correlation between HC NAA/Cr and performance on the constructional praxis subtest of the CERAD battery (r ¼ 0.299, unadjusted p ¼ 0.010). The WM mI/Cr ratio had an inverse correlation with performance on the phonemic fluency subtest with a Pearson's coefficient of 0.295 and an unadjusted p value of 0.012. However, none of the above correlations remained significant after multiple testing correction at a false discovery rate of 5%. 3.4. ROC curve analysis ROC curve analysis of patients with AD and control participants showed that a WM pH value of 6.89 or lower demonstrated optimal ability to discriminate between patients with AD and normal control participants (sensitivity, 76.9%; specificity, 72.4%). In ROC curve analysis of patients with AD and patients with FTLD, a WM pH value of 6.90 or lower discriminated between patients with AD and patients with FTLD with a sensitivity of 88.5% and a specificity of 66.7%. At a cutoff value of 6.87 or lower, the specificity increased to 86.7% but the sensitivity decreased to 69.2%. Analysis of the ROCs of patients with MCI and control participants showed that a WM pH value of 6.88 or lower discriminated between these 2 groups of study subjects (sensitivity, 70.4%; specificity, 72.4%). When the WM pH level was added to the NAA/Cr ratio in the ROC analyses, the diagnostic accuracy was increased, as indicated by the numerical gains in area under the curve (AUC) from the single to the combined marker analyses of patients with AD and control participants; the ROC AUC value of the single-predictor model was 0.821, whereas the two-predictor model combining WM pH and NAA/Cr had an AUC of 0.911 (see Fig. 3). The diagnostic accuracy rose from 76.4% at optimal cutoff of single marker NAA/Cr ratio to 85.5% in the combined model. Notably, the combination of reduced pH and reduced NAA/Cr ratio in WM distinguished patients with AD from control participants with a sensitivity of 100% at a corresponding specificity of 72.4% (Fig. 3). 4. Discussion This study showed a significant negative correlation between pH levels of the HC and age in cognitively healthy participants and significantly lower periventricular WM pH levels in patients with AD but not in patients with other types of dementia. The finding of decreasing pH levels of the HC in association with normal aging is in line with the findings of previous studies (Forester et al., 2010; Roberts and Sick, 1996) and may reflect a known vulnerability of the HC to the aging process (Bartsch and Wulff, 2015). We also found that the mean CB pH level in normal subjects was lower than the pH levels in the supratentorial regions as a possible physiological variation in regional brain pH. Whereas our study found no statistically significant alterations in pH of the HC in patients with AD, MCI, or dementia of any type, we found that pH levels in the periventricular WM were significantly lower than normal in patients with AD and to a lesser extent

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Fig. 2. (A) pH in the white matter of the centrum semiovale across groups (mean  SD). (B) pH in the hippocampus across groups (mean  SD). (C) NAA/Cr in the white matter of the centrum semiovale across groups (mean  SD). Significant differences are indicated by asterisks: *p  0.05, ** p  0.01, and *** p  0.001. Mixed-type dementia is mixed AD and vascular dementia. Frontotemporal dementia spectrum disorders and pure vascular dementia are grouped together under the term “other dementias.” Abbreviations: MCI, mild cognitive impairment; AD, Alzheimer's disease; NAA, N-acetylaspartate; Cr, creatine.

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Fig. 3. Clinical utility of combined MR spectroscopy markers: ROC curves representing single and combined markers as predictors of Alzheimer's disease against cognitively normal controls (AUC for WM pH: 0.809, p < 0.001, 95% CI: 0.70e0.92; AUC for NAA/Cr: 0.821, p < 0.001, CI: 0.71e0.93; AUC for the two-predictor model: 0.911, p < 0.001, CI: 0.83e0.99). Abbreviations: ROC, receiver operating characteristic; AUC, area under the curve; WM, white matter; NAA, N-acetylaspartate; Cr, creatine.

in patients with MCI. WM pH levels were not significantly lower in patients with mixed-type dementia: these patients, as well as patients with MCI, exhibited, however, numerically, mean levels of WM pH in the intermediate range between normality and AD. Because both groups are considered to represent prodromal or coexistent AD, this pattern of gradual pH decline implies a possible relationship with the degree of underlying AD pathology. The absence of significant pH changes in patients with FTD or pure VaD further strengthens this hypothesis. We also found a significantly lower NAA/Cr ratio in the WM of demented patients confirming the results of earlier studies (Graff-Radford and Kantarci, 2013). The decrease in the NAA/Cr ratio in demented patients compared with controls is believed to reflect axonal damage. Interestingly, the results of previous studies have also suggested that the WM is an important site of AD pathology because WM degeneration is markedly present in both preclinical and manifest AD and cannot be fully explained by gray matter atrophy (Collins-Praino et al., 2014) or vascular disease (Bosch et al., 2012; de la Monte, 1989). Furthermore, amyloid beta (Ab) peptides tend to accumulate in the WM independent of cortical plaques severity (Collins-Praino et al., 2014). We observed no variations in cerebellar pH with age or neurodegenerative disease. This is not surprising for AD in view of the fact that the deposition of Ab protein affects the CB only late in the development of beta amyloidosis in AD (Thal et al., 2002). It is assumed that our pH measurements reflect the intracellular environment (Gasparovic et al., 1998). Cells may partly be able to defend pHi in the face of acidic extracellular conditions (Ruffin et al., 2014), which may explain why pHi in the HC or in voxels containing substantial proportions of gray matter was found to be unchanged in our study or slightly increased in another study of mild AD (Rijpma et al., 2018). The biochemical processes that could lead to decreases in cerebral pHi include changes in cellular metabolism and calcium concentrations, mitochondrial dysfunction, and neuroinflammation. Furthermore, glial cell dysfunction in the WM may

be responsible for an uncompensated drop in pH because glial cells indirectly influence neuronal pH by regulating the pH of the extracellular fluid (Chesler, 2003). It remains controversial whether a moderate acidification is part of the pathological process or a protective reaction of neural tissue aimed at preventing harmful overactivation or exacerbation of damage (Uribe-San Martin et al., 2009). Very low pH, on the other hand, can provoke deleterious events for the cell and can even induce neuronal death (Lagadic-Gossmann et al., 2004). Intracellular hydrogen ions at high concentrations may bind with brain proteins, potentially altering their conformation and function as enzymes, transporters, contractile elements, or structural components. The activity of b-secretase, which is crucial in the pathologic cleavage of amyloid precursor protein, is at its highest level in an acidic environment (Vassar et al., 1999). Importantly, acidosis could promote the aggregation of Ab because protein aggregation is a pHdependent process (Atwood et al., 1998; Barrow and Zagorski, 1991; Burdick et al., 1992; Pedersen et al., 2015; Srinivasan et al., 2003; Su and Chang, 2001). In addition, reductions in pH may lead to abnormal hyperphosphorylation of tau proteins (Basurto-Islas et al., 2013) and may enhance the iron-catalyzed production of reactive oxygen species as mediators of cell damage (Li and Siesjo, 1997). Finally, a pH level lower than the crucial value of 6.6 may promote damage of brain capillary endothelial cells (Pirchl et al., 2006) and thus cause a breakdown in amyloid homeostasis and in the defense against circulating neurotoxins. One limitation of this study is the fact that not all subjects were tested in one type of scanner. At the initiation of the study, only the 1.5 T scanner was available; a 3T scanner was added later. This can lead to variation in achievable signal-to-noise ratio, the ability to provide high-resolution spectra from smaller voxels, and chemical shift displacement error for different metabolites. We eliminated the possible effect of this heterogeneity on our results by adding the device used (1.5 T scanner vs 3T scanner) as a covariate in the analysis. When interpreting our results, one should bear in mind

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that the chemical shift displacement error of histidine is larger relative to the other metabolite resonances and different between the 2 field strengths. This could mean that the histidine and other metabolites' signals originate not from fully identical but only partially overlapping anatomical regions in the brain. In particular, regarding histidine information from the voxel placed in the HC, the effect of signal originating from surrounding structures is more pronounced because of the small size of the HC. Another limitation of the study is the imbalance between the numbers of measurements obtained from each of the 3 brain locations, for reasons mainly related to data quality. pH values acquired from the HC and the CB were available for a smaller subset of participants; thus, the statistical power necessary to detect a significant change in these regions was lower compared with the WM. In conclusion, our findings show that changes in pH underlie physiological and pathological brain aging. In agreement with previous reports of nonspecific alterations in dementias (Kantarci et al., 2004), classical metabolite ratio changes could not differentiate between patients with AD and FTLD. WM pH, on the other hand, demonstrated a fair diagnostic accuracy in differentiating not only between patients with AD and controls but also between patients with AD and FTLD. Overall diagnostic accuracy in distinguishing patients with AD from controls improved with the addition of pH to NAA/Cr ratio suggesting that these 2 markers may have complementary value. Proton spectroscopy is the most widely available MRS method; it does not require additional hardware beyond that already used for MRI and is less time-consuming than phosphorus-31 spectroscopy. Our findings of significant differences in pH between young and aged subjects together with the result of low pH in AD but not in other dementias, apart from suggesting a potential mechanism to explain the relationship between brain aging and AD, also provide a first hint about the potential value of in vivo brain pH assessments in assisting the differential diagnosis of AD from other dementias. Disclosure The authors report no conflicts of interest. Acknowledgements The authors acknowledge the facilities provided by the Department of Neuroradiology of the Saarland University Clinic in Homburg. This work was supported by Grants to KF and YL from SNOWBALL, an EU Joint Programme for Neurodegenerative Disease (JPND) (01ED1617B). Authors' contributions: EL contributed to study design, analysis and interpretation of data, drafting and revision of the manuscript, and statistical analysis. AR-S: interpretation of data and revision of the manuscript. PK, AS, and SK contributed to acquisition of data, and revision of the manuscript. MB contributed to study design, acquisition and analysis of data, and revision of the manuscript. YD, AL, WR contributed to revision of the manuscript. ML contributed to analysis of data. KF contributed to study design, interpretation of data, and revision of the manuscript. References American Psychiatric Association, 1994. Diagnostic and Statistical Manual of Mental Disorders, 4th Ed. American Psychiatric Association, Washington, DC. Atwood, C.S., Moir, R.D., Huang, X., Scarpa, R.C., Bacarra, N.M., Romano, D.M., Hartshorn, M.A., Tanzi, R.E., Bush, A.I., 1998. Dramatic aggregation of Alzheimer abeta by Cu(II) is induced by conditions representing physiological acidosis. J. Biol. Chem. 273, 12817e12826.

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